Mplus  
Sunday May 28, 2017 



Time Series Analysis: Dynamic Structural Equation Modeling (DSEM) Time series analysis is used to analyze intensive longitudinal data such as those obtained with ecological momentary assessments, experience sampling methods, daily diary methods, and ambulatory assessments. Such data typically have a large number of time points, for example, twenty to two hundred. The measurements are typically closely spaced in time. Multilevel time series analysis of intensive longitudinal data typically considers time points nested within individuals. Individual differences in level1 parameters such as the mean, variance, and autocorrelation are represented as random effects that are modeled on level 2 in a twolevel analysis. Mplus Version 8, released April 20, 2017, offers twolevel, crossclassified, as well as singlelevel (N=1) time series analysis. In crossclassified analysis the random effects are allowed to vary not only across individuals but also across time to represent timevarying effects. Mplus can estimate a variety of N=1, twolevel and crossclassified time series models. These include univariate autoregressive, regression, crosslagged, confirmatory factor analysis, Item Response Theory, and structural equation models for continuous, binary, ordered categorical (ordinal), or combinations of these variable types. Bayesian analysis is used in the estimation using a flexible latent variable modeling framework referred to as dynamic structural equation modeling (DSEM). DSEM Theory The following papers discuss multilevel time series analysis modeling and estimation:
DSEM Applications The following paper discusses multilevel time series analysis applications:
DSEM Webinars DSEM in Mplus Version 8 was presented to the Prevention Science Methodology Group (PSMG) in March and April 2017. Following are links to videos and handouts from these occasions:
DSEM Examples in the Mplus Version 8 User’s Guide
